Yu Zhe, Li Yong, Xing Tingyan, Han Ming, Zhang Yaohua, Gao Jinrong, Du Jing, Li Jing, Zeng Qi, Chen Xueli
Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital, Affiliated People's Hospital of Northwest University), No 4. Jiefang Road, Xin-Cheng District, Xi'an 710004, Shaanxi, China.
Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
Biomed Opt Express. 2024 Dec 3;16(1):28-41. doi: 10.1364/BOE.539721. eCollection 2025 Jan 1.
The study aimed to identify differences in the biochemical composition of corneal stroma lenses across varying degrees of myopia using Raman spectrum characteristics. Corneal stroma lens samples from 38 patients who underwent small incision lens extraction (SMILE) surgery, were categorized into low (n = 9, spherical power -3.00D), moderate (n = 23, spherical power < -3.00D and > -6.00D), and high myopia (n = 6, spherical power ≦-6.00D) groups. A custom-built microscopic confocal Raman system (MCRS) was used to collect Raman spectra, which were processed by smoothing, denoising, and baseline calibrating to refine raw data. Independent sample t-tests were used to analyze spectral feature peaks among sample types. Significant differences ( < 0.001) were found in multiple Raman spectral characteristic peaks (854 cm, 937 cm, 1002 cm, 1243 cm, 1448 cm, and 2940 cm) between low and high myopia samples, particularly at 2940 cm. Differences were also found between low and moderate, and moderate and high myopia samples, although fewer than between low and high myopia samples. The three-classification model, particularly with PLS-KNN training, exhibited superior discriminative performance with accuracy rates of 95%. Similarly, the two-classification model for low and high myopia achieved high accuracy with PLS-KNN (94.4%) compared to PCA-KNN (93.3%). PLS dimensionality reduction slightly outperformed PCA, enhancing classification accuracy. In addition, in both reduction methods, the KNN algorithm demonstrated the highest accuracy and performance. The optimal PLS-KNN classification model showed AUC values of 0.99, 0.98, and 1.00 for ROC curves corresponding to low, moderate, and high myopia, respectively. Classification accuracy rates were 89.7% and 96.9%, and 100% for low and high myopia, respectively. For the two-classification model, accuracy reached 94.4% with an AUC of 0.98, indicating strong performance in distinguishing between high and low myopic corneal stroma. We found significant biochemical differences such as collagen, lipids, and nucleic acids in corneal stroma lenses across varying degrees of myopia, suggesting that Raman spectroscopy holds substantial potential in elucidating the pathogenesis of myopia.
该研究旨在利用拉曼光谱特征,识别不同近视程度的角膜基质透镜在生化组成上的差异。对38例接受小切口透镜切除术(SMILE)的患者的角膜基质透镜样本进行分类,分为低度近视组(n = 9,球镜度数-3.00D)、中度近视组(n = 23,球镜度数<-3.00D且>-6.00D)和高度近视组(n = 6,球镜度数≤-6.00D)。使用定制的显微共焦拉曼系统(MCRS)收集拉曼光谱,并通过平滑、去噪和基线校准对原始数据进行处理。采用独立样本t检验分析不同样本类型之间的光谱特征峰。在低度近视和高度近视样本之间,多个拉曼光谱特征峰(854 cm、937 cm、1002 cm、1243 cm、1448 cm和2940 cm)存在显著差异(<0.001),尤其是在2940 cm处。低度近视与中度近视样本之间以及中度近视与高度近视样本之间也存在差异,不过比低度近视与高度近视样本之间的差异要少。三分类模型,特别是采用偏最小二乘法-近邻算法(PLS-KNN)训练时,具有卓越的判别性能,准确率达95%。同样,低度近视和高度近视的二分类模型采用PLS-KNN时的准确率较高(94.4%),高于主成分分析-近邻算法(PCA-KNN)(93.3%)。偏最小二乘法降维略优于主成分分析,提高了分类准确率。此外,在两种降维方法中,近邻算法(KNN)的准确率和性能最高。最佳的PLS-KNN分类模型对低度、中度和高度近视对应的ROC曲线的AUC值分别为0.99、0.98和1.00。低度和高度近视的分类准确率分别为89.7%和96.9%,以及100%。对于二分类模型,准确率达到94.4%,AUC为0.98,表明在区分高度和低度近视角膜基质方面性能强劲。我们发现不同近视程度的角膜基质透镜在胶原蛋白、脂质和核酸等生化方面存在显著差异,这表明拉曼光谱在阐明近视发病机制方面具有巨大潜力。